Discarge compilation from The Global River Discharge (RivDIS) Project

The Global River Discharge (RivDIS) data set contains monthly discharge measurements for 1018 stations located throughout the world. The period of record varies widely from station to station, with a mean of 21.5 years. These data were digitized from published UNESCO archives by Charles Voromarty, Balaze Fekete, and B.A. Tucker of the Complex Systems Research Center (CSRC) at the University of New Hampshire. River discharge is typically measured through the use of a rating curve that relates local water level height to discharge. This rating curve is used to estimate discharge from the observed water level. The rating curves are periodically rechecked and recalibrated through on-site measurement of discharge and river stage.

A Prediction Model to Diabetes using Artificial Metaplasticity

Diabetes is the most common disease nowadays in all populations and in all age groups. Different techniques of artificial intelligence has been applied to diabetes problem. This research proposed the artificial metaplasticity on multilayer perceptron (AMMLP) as prediction model for prediction of diabetes. The Pima Indians diabetes was used to test the proposed model AMMLP. The results obtained by AMMLP were compared with other algorithms, recently proposed by other researchers, that were applied to the same database. The best result obtained so far with the AMMLP algorithm is 89.93%